The olfactory system provides a useful model for understanding how information about the outside world is represented as patterns, or codes, of neural activity. Olfactory coding begins in olfactory receptor neurons. These neurons can be spontaneously active, but modify their spiking output when they encounter odorants. With electrophysiological recordings from olfactory receptor neuronsand from three successive layers of follower cells, and with computational models, we explored how the olfactory system extracts signal from noise, and generates informative patterns of activity that represent different odorants. To investigate fundamental questions about information coding by neurons, we focused on relatively simple animals, insects. In the insect olfactory system, olfactory receptor neurons and second order projection neurons and local neurons of the antennal lobe exhibit high baseline activity in the absence of deliberately delivered odor stimuli. However, third-order neurons, Kenyon Cells, exhibit very low baseline activity, and very sparse responses to odors, under the same conditions. (In vertebrates, similar observations have been made in first and second order olfactory neurons.) We used the locust olfactory system to explore where baseline activity originates and how it propagates through multiple layers of neurons. To locate the source of baseline activity, we reversibly silenced the receptor neurons by specifically cooling the antenna while tracking activity in each type of downstream cell. Cooling the antenna significantly lowered the spontaneous firing rate of receptor neurons and had three main effects downstream. First, odor responses were eliminated. Second, spontaneous spiking in projection neurons was nearly eliminated. Third, as the antenna cooled, the resting membrane potentials of the projection and local neurons, and the Kenyon cells significantly decreased. These results demonstrate that the olfactory receptor neurons provide a constant barrage of spontaneous input that propagates to later stages of olfactory processing. This input contributes to determining the spike rates of projection neurons and helps determine the resting membrane potentials of higher-order neurons. With a simple Receiver-Operator Characteristic model we demonstrated discriminating signal from noise is best when firing thresholds in projection neurons are relatively low and thresholds in Kenyon cells are relatively high. This configuration permits the maximal convergence of information, and allows the transformation signals arising in highly sensitive but noisy ORNs into very sparse codes in KCs within two layers of neurons. Our exploration of noise sources in locust olfaction provides a specific example of how a detection system, bombarded by noise at the first stage of processing, balances the competing challenges of maintaining sensitivity to a wide range of stimuli and setting thresholds to eliminate noise and sparsen internal sensory representations. These principles may apply to other natural or artificial sensory systems that employ multiple stages of processing and circuitry convergence to achieve optimal detection. Pulses of odorants are represented as spatiotemporal patterns of spikes in neurons of the antennal lobe (insects) and olfactory bulb (vertebrates). These response patterns have been thought to arise primarily from interactions within the antennal lobe and the olfactory bulb, an idea supported, in part, by the assumption that olfactory receptor neurons respond to odorants with simple firing patterns. However, activating the antennal lobe directly with simple pulses of current evoked responses in antennal lobe neurons that were much less diverse, complex, and enduring than responses elicited by odorants. Similarly, models of the antennal lobe driven by simplistic inputs generated relatively simple output. How then are dynamic neural codes for odors generated? Consistent with recent results from several other species, our recordings from locust olfactory receptor neurons showed a great diversity of temporal structure. Furthermore, we found that, viewed as a population, many response features of ORNs were remarkably similar to those observed within the antennal lobe. Using a set of computational models constrained by our electrophysiological recordings, we found that the temporal heterogeneity of responses of olfactory receptor neurons critically underlies the generation of spatiotemporal odor codes in the antennal lobe. A test then performed in vivo confirmed that, given temporally homogeneous input, the antennal lobe cannot create diverse spatiotemporal patterns on its own;however, given temporally heterogeneous input, the antennal lobe generated realistic firing patterns. Finally, given the temporally structured input provided by olfactory receptor neurons, we clarified several separate, additional contributions of the AL to olfactory information processing. Thus, our results demonstrate the origin and subsequent reformatting of spatiotemporal neural codes for odors.